A reinforcement learning approach for process parameter optimization in additive manufacturing

Process optimization for metal additive manufacturing (AM) is crucial to ensure repeatability, control microstructure, and minimize defects. Despite efforts to address this via the traditional design of experiments and statistical process mapping, there is limited insight on an on-the-fly optimization framework that can be integrated into a metal AM system. Additionally, most of these methods, being data-intensive, cannot be supported by a metal AM alloy or system due to budget restrictions. To tackle this issue, the article introduces a Reinforcement Learning (RL) methodology transformed into an optimization problem in the realm of metal AM. An off-policy RL framework based on Q-learning is proposed to find optimal laser power (P)- scan velocity (v) combinations with the objective of maintaining steady-state melt pool depth. For this, an experimentally validated Eagar–Tsai formulation is used as a digital twin emulating the laser-directed energy deposition (L-DED) environment, where the laser operates as the agent across the P−v space such that it maximizes rewards for a melt pool depth closer to the optimum. The culmination of the training process yields a Q-table where the state (P,v) with the highest Q-value corresponds to the optimized process parameters. For a desired melt pool depth of 1 mm for SS316L, the proposed algorithm predicts an optimal P−v combination of 888.9 W - 566.7 mm/min that yields a melt pool depth within 50 μm of the experimental observation. The framework, therefore, provides a model-free approach to learning without any prior.